Optimal Hybrid Electric Vehicle Powertrain Control Based on Route and Speed Optimization

An optimal powertrain controller combining feed-forward and feedback modules is developed based on route and speed optimization for improved fuel economy of hybrid electric vehicle. The economic route and speed is optimized for the given origin-destination with expected trip time by a genetic algorithm based co-optimization method using the traffic data and vehicle characteristics. The feedforward module is based on a global power distribution strategy and the feedback module is a receding horizon linear quadratic tracking control. A cosimulation model, combining traffic model based on SUMO and Simulink hybrid powertrain model, is developed and used for validating the proposed optimal control strategy. The cosimulation results indicate that the proposed control strategy is able to decrease the fuel consumption by up to 30% comparing with the power follower adopting the fastest route. Note that even with the same powertrain controller, the economic route and speed can also improve the fuel economy by 14.21% comparing with the fastest route without optimization.